AI Data Scientist

AI Data Scientist – MLOps/LLMOps | GCP | GenAI-5+ Years-PAN INDIA

Summary

Exciting opportunity for an experienced AI Data Scientist with strong expertise in MLOps, LLMOps, Generative AI, and cloud-native AI platforms. The role involves building scalable machine learning and LLM pipelines, deploying enterprise-grade AI solutions on GCP, and driving the end-to-end AI lifecycle from development to production. This is an excellent opportunity to work on cutting-edge technologies including Vertex AI, RAG architectures, Vector Databases, LangChain, Kubernetes, and Large Language Models.

Location

Noida | Gurugram | Bengaluru | Pune (Hybrid)

Your Future Employer

A leading global analytics and digital transformation organization, helping enterprises leverage AI, Machine Learning, and Generative AI technologies to build next-generation intelligent solutions. The organization offers an innovative work environment, exposure to large-scale enterprise AI implementations, and opportunities to work on advanced cloud-native AI architectures.

Responsibilities

• Design, develop, and maintain scalable MLOps and LLMOps pipelines for Machine Learning and Generative AI applications on Google Cloud Platform.
• Build and operationalize end-to-end ML workflows including data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining.
• Develop and manage LLMOps frameworks including prompt orchestration, RAG pipelines, vector databases, model evaluation, guardrails, and observability.
• Deploy and manage ML and GenAI workloads using Vertex AI, GKE, Cloud Run, BigQuery, and other GCP-native services.
• Implement CI/CD and Continuous Training pipelines using GitHub Actions, Jenkins, Terraform, Cloud Build, and related DevOps technologies.
• Establish monitoring frameworks for model performance, drift detection, latency tracking, observability, and operational metrics.
• Manage model registry, feature stores, metadata tracking, experiment management, and pipeline orchestration.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, and Product Teams to productionize AI and GenAI solutions.
• Drive responsible AI practices, governance controls, model auditability, security compliance, and access management.
• Optimize inference workloads for scalability, reliability, performance, and cost efficiency.
• Support production environments through troubleshooting, debugging, incident resolution, and continuous platform improvements.

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Engineering, or related discipline.
• 5–7 years of experience in Machine Learning Engineering, MLOps, ML Platform Engineering, or LLMOps.
• Strong hands-on experience with GCP services including Vertex AI, BigQuery, GCS, Cloud Run, GKE, Pub/Sub, IAM, and Cloud Functions.
• Proven expertise in building LLMOps workflows including RAG pipelines, vector databases, prompt engineering, evaluation frameworks, and LLM observability.
• Strong programming skills in Python, SQL, and automation scripting.
• Hands-on expertise with Docker, Kubernetes, and cloud-native deployments.
• Experience with orchestration frameworks such as Kubeflow, Vertex AI Pipelines, Airflow, or similar tools.
• Strong understanding of CI/CD, Infrastructure as Code (Terraform), GitHub Actions, Jenkins, Cloud Build, and Git.
• Experience with MLflow, Vertex AI Model Registry, or equivalent model tracking and experiment management platforms.
• Knowledge of feature stores, model monitoring, drift detection, logging frameworks, and production ML support.
• Exposure to LangChain, LangGraph, LlamaIndex, Hugging Face, and modern LLM ecosystems.
• Experience working with vector databases such as Pinecone, Weaviate, Chroma, or Vertex AI Vector Search.
• Strong understanding of cloud security, IAM, governance, secrets management, and responsible AI principles.
• Excellent stakeholder management, communication, and problem-solving skills.
• Experience with real-time, scalable, and highly available enterprise AI deployments will be an advantage.

What’s in it for you?

• Opportunity to work on cutting-edge AI, GenAI, and LLM technologies.
• Exposure to enterprise-scale MLOps and cloud-native AI deployments.
• Collaborative and innovation-driven work environment.
• Strong career progression and learning opportunities in AI Engineering and Platform Development.
• Opportunity to contribute to transformative AI programs impacting global businesses.

Reach Us

If you feel this opportunity is well aligned with your career progression plans, please feel free to reach me with your updated profile at rishabh.midha@crescendogroup.in for a confidential discussion on the role.

Disclaimer

Crescendo Global specializes in Senior to C-level niche recruitment. We are passionate about empowering job seekers and employers with an engaging and memorable job search and leadership hiring experience. Crescendo Global does not discriminate on the basis of race, religion, color, origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Note

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Keywords

AI Data Scientist, MLOps, LLMOps, Machine Learning Engineer, Generative AI, GenAI, Vertex AI, Google Cloud Platform, GCP, Kubernetes, Docker, Kubeflow, MLflow, LangChain, LangGraph, LlamaIndex, RAG, Vector Database, Pinecone, Chroma, Weaviate, Prompt Engineering, Model Monitoring, Drift Detection, CI/CD, Terraform, Cloud Run, GKE, BigQuery, Feature Store, AI Platform Engineering, NLP, Deep Learning, Machine Learning, Python, SQL, Cloud AI, Responsible AI.